scholarly journals Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction

2021 ◽  
Vol 11 (16) ◽  
pp. 7334
Author(s):  
Rongyao Yuan ◽  
Chao Su ◽  
Enhua Cao ◽  
Shaopei Hu ◽  
Heng Zhang
Keyword(s):  
Prediction Models ◽  
Short Term Memory ◽  
Negative Impact ◽  
Reference Value ◽  
Original Data ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Deformation Prediction ◽  
Multi Scale ◽  
Dam Deformation

Affected by various complex factors, dam deformation monitoring data usually reflect volatility and non-linear characteristics, and traditional prediction models are difficult to accurately capture the complex laws of dam deformation. A multi-scale deformation prediction model based on Variational Modal Decomposition (VMD) signal decomposition technology is proposed in this study. The method first decomposes the original deformation sequence into a series of sub-sequences with different frequencies, then the decomposed sub-sequences are modeled and predicted by Long Short-Term Memory neural network (LSTM) and Random Forest (RF) according to different frequencies. Finally, the prediction results of all sub-sequences are reconstructed to obtain the final deformation prediction results. In this process, it is proposed to use the instantaneous frequency mean method to determine the decomposition modulus of VMD. The innovation of this paper is to decompose the monitoring data with high volatility, and use LSTM and RF prediction, respectively, according to the frequency of the monitoring data, so as to realize the more accurate capture of volatility data during the prediction process. The case analysis results show that the proposed model can effectively solve the negative impact of the original data volatility on the prediction results, and is superior to the traditional prediction models in terms of stability and generalization ability, which has an important reference value for accurately predicting dam deformation and has far-reaching engineering significance.

scholarly journals A Fractal Prediction Method for Safety Monitoring Deformation of Core Rockfill Dams

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liang Pei ◽  
Jiankang Chen ◽  
Jingren Zhou ◽  
Huibao Huang ◽  
Zhengjun Zhou ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Prediction Method ◽  
Safety Monitoring ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Dimension Theory ◽  
Deformation Prediction ◽  
The Core ◽  
Dam Deformation ◽  
And Control

Deformation mechanism in the core rockfill dams with heavy load and high-stress level is difficult to predict and control, which is one of the key problems to be solved in the dam operation safety management and control. Aiming at the large error problems obtained by the parameter-based functional models (regression model, grey theory model, etc.) in the deformation prediction of the core rockfill dams, a fractal prediction method and its technical process by combining the variable dimension fractal dimension and the "metabolism" of prediction data are proposed through analyzing the fractal adaptability and deformation characteristics of original monitoring data based on the resealed-range (R/S) method and fractal dimension theory. It effectively solves the error in the process of constant dimension fractal accumulation and transformation greatly in dam deformation prediction and provides a new way for dam safety monitoring deformation prediction and early warning. The trend analysis of deformation monitoring data of the Pubugou core rockfill dam and the deformation prediction show that the fractal prediction information of dam deformation has a good corresponding relationship with its physical causes, which is in line with the actual deformation trend and operation state of the dam. Compared with the traditional stepwise regression method, the prediction results obtained by the proposed method in this paper are of high accuracy, implying that the improved fractal prediction of dam deformation is effective and the Hurst fractal index is applicable in the evaluation of the dam deformation trend.

scholarly journals Dam Deformation Interpretation and Prediction Based on a Long Short-Term Memory Model Coupled with an Attention Mechanism

2021 ◽  
Vol 11 (14) ◽  
pp. 6625
Author(s):  
Yan Su ◽  
Kailiang Weng ◽  
Chuan Lin ◽  
Zeqin Chen
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Impact Factors ◽  
Nonlinear Prediction ◽  
Time Dimension ◽  
Short Term ◽  
Deformation Prediction ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Dam Deformation

An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.

scholarly journals Dam Deformation Monitoring Data Analysis Using Space-Time Kalman Filter

2016 ◽  
Vol 5 (12) ◽  
pp. 236 ◽  
Author(s):  
Wujiao Dai ◽  
Ning Liu ◽  
Rock Santerre ◽  
Jiabao Pan
Keyword(s):  
Kalman Filter ◽  
Data Analysis ◽  
Space Time ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Dam Deformation

Application of Self-Adaptive MGM (1, n) Model in Dam Deformation Prediction

2013 ◽  
Vol 740 ◽  
pp. 284-288
Author(s):  
He Zhi Liu ◽  
Song Lin Wang ◽  
Jing Yang Liu ◽  
Guang Yang
Keyword(s):  
Estimation Method ◽  
Original Data ◽  
Arch Dam ◽  
Deformation Prediction ◽  
Grey Forecasting ◽  
Forecasting Method ◽  
Forecasting Performance ◽  
Dam Deformation ◽  
Data Group ◽  
Self Adaptive

Considering the characteristics of randomness and uncertainty of dam system and the lack of safety monitoring data in some projects, a grey forecasting method based on self-adaptive MGM (1, n) was proposed in this paper to predict the dam deformation. Firstly, theory of the traditional MGM (1, n) model and the parameter estimation method were introduced. On the basis of this, add these forecasted values into the original data group and eliminate the oldest information, the self-adaptive MGM (1, n) model could be established. This paper employs this improved approach in the dam deformation of an arch dam. By predicting the dam deformation in next 5 days, the validity of such method was proved. Compared with GM (1, 1) model and conventional MGM (1, n) model, the experimental results indicate that the forecasting performance is significantly superior to that of the above mentioned two methods.

Study on the Applications of Neural Networks for Processing Deformation Monitoring Data

2014 ◽  
Vol 501-504 ◽  
pp. 2149-2153 ◽  
Author(s):  
Cai Yun Gao ◽  
Xi Min Cui ◽  
Xue Qian Hong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Prediction Models ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Nonlinear Fitting ◽  
High Rise ◽  
High Rise Building ◽  
Good Ability ◽  
Neural Network Prediction

Accurately estimating the deformation of high-rise building is a very important work for surveyors, however it is very difficult to get an accurate and reliable predictor. In this paper, artificial neural network has been applied here because of its good ability of nonlinear fitting. On the basis of the high-rise building monitoring data, three prediction models including the BP, RBF and GRNN neural network prediction models were established, the comparative analysis for the prediction accuracy of the three models was obtained. The results show that neural network is capable for prediction, and GRNN possess higher capability in prediction and better adaptability in comparing with other two neural networks.

Dam Deformation Prediction Based on EEMD-ELM Model

2020 ◽  
Author(s):  
Tao Yan ◽  
Bo Chen
Keyword(s):  
Training Sample ◽  
Ensemble Empirical Mode Decomposition ◽  
Deformation Monitoring ◽  
Concrete Gravity Dam ◽  
Deformation Prediction ◽  
Monitoring Model ◽  
Mode Decomposition ◽  
Dam Deformation ◽  
Learning Machine ◽  
Bpnn Model

<p>Establishing a reasonable and reliable dam deformation monitoring model is of great significance for effective analysis of dam deformation monitoring data and accurate assessment of dam working conditions. Firstly, the dam deformation is decomposed by the EEMD algorithm to obtain IMF components representing different characteristic scales, and different influencing factors are selected for different IMF components. Secondly, each IMF component is used as the ELM training sample to analyze, fit and predict the dam deformation component. Finally, the prediction results of each IMF component are accumulated to obtain the dam deformation prediction value. Taking a roller compacted concrete gravity dam as an example, the EEMD-ELM model is used to predict the deformation of the dam. At the same time, it is compared and analyzed with the prediction results of the BPNN model and the ELM model. The mean square error of the EMD-ELM model is 0.566, which is 54% and 14.8% lower than the BPNN model and the ELM model, indicating that the EEMD-ELM model has higher prediction accuracy and has certain application value.</p><p><strong>Key words:</strong> dam deformation;prediction model; ensemble empirical mode decomposition; extreme learning machine</p>

scholarly journals A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces

2021 ◽  
Vol 11 (1) ◽  
pp. 463
Author(s):  
Hao Gu ◽  
Tengfei Wang ◽  
Yantao Zhu ◽  
Cheng Wang ◽  
Dashan Yang ◽  
...  
Keyword(s):  
Missing Data ◽  
Back Propagation ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Concrete Dam ◽  
Human Errors ◽  
Data Completion ◽  
Deformation Behaviors ◽  
Dam Deformation ◽  
Deformation Patterns

A concrete dam is an important water-retaining hydraulic structure that stops or restricts the flow of water or underground streams. It can be regarded as a constantly changing complex system. The deformation of a concrete dam can reflect its operation behaviors most directly among all the effect quantities. However, due to the change of the external environment, the failure of monitoring instruments, and the existence of human errors, the obtained deformation monitoring data usually miss pieces, and sometimes the missing pieces are so critical that the remaining data fail to fully reflect the actual deformation patterns. In this paper, the composition, characteristics, and contamination of the concrete dam deformation monitoring information are analyzed. From the single-value missing data completion method based on the nonlocal average method, a multi-value missing data completion method using BP (back propagation) mapping of spatial adjacent points is proposed to improve the accuracy of analysis and pattern prediction of concrete dam deformation behaviors. A case study is performed to validate the proposed method.

scholarly journals A Deep Learning Model for Concrete Dam Deformation Prediction Based on RS-LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Xudong Qu ◽  
Jie Yang ◽  
Meng Chang
Keyword(s):  
Prediction Model ◽  
Evaluation System ◽  
Prediction Models ◽  
Evaluation Index ◽  
Accuracy Evaluation ◽  
Engineering Project ◽  
Deformation Prediction ◽  
Concrete Dam ◽  
Evaluation Indexes ◽  
Dam Deformation

Deformation is a comprehensive reflection of the structural state of a concrete dam, and research on prediction models for concrete dam deformation provides the basis for safety monitoring and early warning strategies. This paper focuses on practical problems such as multicollinearity among factors; the subjectivity of factor selection; robustness, externality, generalization, and integrity deficiencies; and the unsoundness of evaluation systems for prediction models. Based on rough set (RS) theory and a long short-term memory (LSTM) network, single-point and multipoint concrete dam deformation prediction models for health monitoring based on RS-LSTM are studied. Moreover, a new prediction model evaluation system is proposed, and the model accuracy, robustness, externality, and generalization are defined as quantitative evaluation indexes. An engineering project shows that the concrete dam deformation prediction models based on RS-LSTM can quantitatively obtain the representative factors that affect dam deformation and the importance of each factor relative to the effect. The accuracy evaluation index (AVI), robustness evaluation index (RVI), externality evaluation index (EVI), and generalization evaluation index (GVI) of the model are superior to the evaluation indexes of existing shallow neural network models and statistical models according to the new evaluation system, which can estimate the comprehensive performance of prediction models. The prediction model for concrete dam deformation based on RS-LSTM optimizes the factors that influence the model, quantitatively determines the importance of each factor, and provides high-performance, synchronous, and dynamic predictions for concrete dam behaviours; therefore, the model has strong engineering practicality.

Dam Deformation Monitoring and Data Analysis Based on Cloud Model

2012 ◽  
Vol 459 ◽  
pp. 479-482 ◽  
Author(s):  
Teng Jun Wang ◽  
Bo Yang ◽  
Hai Yan Yang
Keyword(s):  
Data Analysis ◽  
Quantitative Analysis ◽  
Qualitative Analysis ◽  
Cloud Model ◽  
Scientific Data ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Monitoring Method ◽  
Reliable Monitoring ◽  
Dam Deformation

Dam deformation monitoring plays an important role in order to ensure the safety of dam operation, to improve project efficiency and the level of design and construction. Reliable monitoring method and scientific data analysis is the best protection for control the deformation law. Mathematical methods have been used to precisely quantitative analysis the deformation of the dam monitoring points. Usually, when assess the stability of deformation and evaluate the monitored data, qualitative languages are used to analyze qualitative result. The article combines cloud model with reliable monitoring data of Xiaolangdi to try to make qualitative analysis result quantitatively, and the quantitative analysis result can verify the qualitative analysis conclusion. It has realized the change between those two analyses. Also, utilize cloud model to analyzing deformation monitoring data is verified reliable.

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